2.2.1 register_parameter(name,param) 为模型添加parameter参数 class Example(nn.Module): def __init__(self): super(Example, self).__init__() print('看看我们的模型有哪些parameter:\t', self._parameters, end='\n') # 输出 mymodel = Example() # ''' # 看看我们的模型有哪些parameter: Ord...
PyTorch batch normalization 2d is a technique to construct the deep neural network and the batch norm2d is applied to batch normalization above 4D input. Syntax: The following syntax is of batch normalization 2d. torch.nn.BatchNorm2d(num_features,eps=1e-05,momentum=0.1,affine=True,track_runnin...
常见的 in-place 操作,比如有:[BatchNorm2d](https://link.zhihu.com/?target=https%3A//pytorch.org/docs/stable/generated/torch.nn.BatchNorm2d.html%23torch.nn.BatchNorm2d) 和[spectral_norm()](https://link.zhihu.com/?target=https%3A//pytorch.org/docs/stable/generated/torch.nn.utils.spectral...
self.bn2 = nn.BatchNorm2d(32) self.conv3 = nn.Conv2d(32, 32, kernel_size=5, stride=2) self.bn3 = nn.BatchNorm2d(32) # Number of Linear input connections depends on output of conv2d layers # and therefore the input image size, so compute it. def conv2d_size_out(size, ...
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (layer1): Sequential( ...
BatchNorm2d层对输入应用规范化以获得零均值和单位方差并提高网络精度。 MaxPool层将帮助我们确保图像中对象的位置不会影响神经网络检测其特定特征的能力。 Linear层是网络中的最后一层,它计算每个类的分数。 在 CIFAR10 数据集中,有 10 类标签。 得分最高的标签将是模型预测的那一个。 在线性层中,必须指定输入特...
nn.BatchNorm2d(8), ) self.fc1 = nn.Sequential( nn.Linear(8*100*100,500), nn.ReLU(inplace=True), nn.Linear(500,500), nn.ReLU(inplace=True), nn.Linear(500,5))defforward_once(self, x): output = self.cnn1(x) output = output.view(output.size()[0], -1) ...
nn.BatchNorm2d(32), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2)) self.fc = nn.Linear(7*7*32, num_classes) def forward(self, x): out = self.layer1(x) out = self.layer2(out) out = out.reshape(out.size(0), -1) out = self.fc(out) return out def train(gpu, arg...
pytorch-sync-batchnorm-example The default behavior of Batchnorm, in Pytorch and most other frameworks, is to compute batch statistics separately for each device. Meaning that, if we use a model with batchnorm layers and train on multiple GPUs, batch statistics will not reflect the wholebatch;...
self.conv1=nn.Conv2d(3,64,kernel_size=7,stride=2,padding=3,bias=False)self.bn1=nn.BatchNorm2d(64)self.relu=nn.ReLU(inplace=True)self.maxpool=nn.MaxPool2d(kernel_size=3,stride=2,padding=1)# 中间卷积部分 self.layer1=self._make_layer(block,64,layers[0])self.layer2=self._make_lay...